Facial recognition techniques continue to evolve as the need for security and identification systems become increasingly important. Attempting to verify (1:1) or identify (1:N) a face with a given gallery data set is challenging.
One such challenge involves identification on very large acquired physiological and behavioral biometric datasets. In particular, there can be dozens of features that may be collected and stored for each sample. As database sizes increase, exhaustive identification searches that attempt to match an inputted set of data with entire biometric meta-feature sets become computationally unmanageable.
Accordingly, a need exists to address the ever growing meta-feature space associated with biometric data sets.